AMFG has unveiled its new Post-Processing Scheduling and digital quality assurance management features for its additive manufacturing workflow automation software.

The company will be demonstrating the new qualities of its flagship product at Formnext this week.

Post-Processing Scheduling afford users the ability to plan and allocate resources ahead of and during the post-processing stage of the additive manufacture of a part. It automatically moves completed builds to the software’s post-processing page, for actions to be allocated and prioritised. The date and time of updates to the progress of the part through the process are logged in the system for full traceability.

Meanwhile, the digital quality assurance tool is being trialled as a beta feature. This feature lets users import para documentation, be it reports, data sheets, or 3D images, and compare them against the physical 3D printed part. Again, the software logs the data history for each part, meaning users have access to the data specifications without having to leave the platform. Thanks to connectivity and scanning systems and sensors, users can have a full view of the dimension and parameters of its parts.

They’re additions to the software that streamline the additive manufacturing process, and store data for future reference. Meanwhile, efforts continue to supplement these latest additions to the platform that will allow the software to explore the reasons for part failures, and prevent them ahead of the build.

“The range of post-processing actions required after production can vary enormously according to technology, material used, and other factors,” commented AMFG CEO, Keyvan Karimi. “Our Post-Processing Scheduling solution simplifies this process by allowing companies to optimise their resources, and provides the right sequence necessary to ensure that parts are finished to the correct specifications.

“We’ve committed to ensuring continuous production for AM. Our robust production management solutions, in addition to our latest offerings for post-production management, ensure that there is a fluid flow across the production process. We’re currently working on developing ways to enrich this even further by leveraging our machine learning technology, for example, to investigate the root causes of any build failures and provide corrective actions.”